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Beyond automatic differentiation
Google AI Blog ai.googleblog.com
Derivatives play a central role in optimization and machine learning. By locally approximating a training loss, derivatives guide an optimizer toward lower values of the loss. Automatic differentiation frameworks such as TensorFlow, PyTorch, and JAX are an essential part of modern machine learning, making it feasible to use gradient-based optimizers to train very complex models.
But are derivatives all we need? By themselves, derivatives only tell us how a …
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